Name
Title | ||
|---|---|---|
Validation and Parameter Sensitivity Tests for Reconstructing Swell Field Based on an Ensemble Kalman Filter. |
Abstract | ||
|---|---|---|
The swell propagation model built on geometric optics is known to work well when simulating radiated swells from a far located storm. Based on this simple approximation, satellites have acquired plenty of large samples on basin-traversing swells induced by fierce storms situated in mid-latitudes. How to routinely reconstruct swell fields with these irregularly sampled observations from space via known swell propagation principle requires more examination. In this study, we apply 3-h interval pseudo SAR observations in the ensemble Kalman filter (EnKF) to reconstruct a swell field in ocean basin, and compare it with buoy swell partitions and polynomial regression results. As validated against in situ measurements, EnKF works well in terms of spatial-temporal consistency in far-field swell propagation scenarios. Using this framework, we further address the influence of EnKF parameters, and perform a sensitivity analysis to evaluate estimations made under different sets of parameters. Such analysis is of key interest with respect to future multiple-source routinely recorded swell field data. Satellite-derived swell data can serve as a valuable complementary dataset to in situ or wave re-analysis datasets. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/s16122000 | SENSORS |
Keywords | Field | DocType |
pseudo SAR observation,EnKF,swell field reconstruction,parameter sensitivity | Data mining,Buoy,Satellite,Field data,Remote sensing,Polynomial regression,Storm,Electronic engineering,Geometrical optics,Engineering,Swell,Ensemble Kalman filter | Journal |
Volume | Issue | Citations |
16 | 12.0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuan Wang | 1 | 0 | 0.34 |
| Pierre Tandeo | 2 | 3 | 3.79 |
| Ronan Fablet | 3 | 312 | 47.04 |
| Romain Husson | 4 | 0 | 0.34 |
| Lei Guan | 5 | 2 | 3.79 |
| Ge Chen | 6 | 5 | 5.31 |